PatternLab for proteomics is a one-stop shop computational environment for analyzing shotgun proteomic data. Its modules provide means to pinpoint proteins/peptides that are differentially expressed and those that are unique to a state. It can also cluster the ones that share similar expression profiles in time-course experiments, as well as help in interpreting results according to Gene Ontology. PatternLab is user-friendly, simple, and provides a graphical user interface.

This unit includes a Basic Protocol with an introduction to the Map Viewer, describing how to perform a simple text-based search of genome annotations to view the genomic context of a gene, navigate along a chromosome, zoom in and out, and change the displayed maps to hide and show information. It also describes some of NCBI's sequence-analysis tools, which are provided as links from the Map Viewer. The Alternate Protocols describe different ways to query the genome sequence, and also illustrate additional features of the Map Viewer. Alternate Protocol 1 shows how to perform and interpret the results of a BLAST search against the human genome. Alternate Protocol 2 demonstrates how to retrieve a list of all genes between two STS markers. Finally, Alternate Protocol 3 shows how to find all annotated members of a gene family.

The DFCI Gene Index Web pages provide access to analyses of ESTs and gene sequences for nearly 114 species, as well as a number of resources derived from these. Each species-specific database is presented using a common format with a home page. A variety of methods exist that allow users to search each species-specific database. Methods implemented currently include nucleotide or protein sequence queries using WU-BLAST, text-based searches using various sequence identifiers, searches by gene, tissue and library name, and searches using functional classes through Gene Ontology assignments. This protocol provides guidance for using the Gene Index Databases to extract information.

Transcription profiling with microarrays has become a standard procedure for comparing the levels of gene expression between pairs of samples, or multiple samples following different experimental treatments. New technologies, collectively known as next-generation DNA sequencing methods, are also starting to be used for transcriptome analysis. These technologies, with their low background, large capacity for data collection, and dynamic range, provide a powerful and complementary tool to the assays that formerly relied on microarrays. In this chapter, we describe two protocols for working with microarray data from pairs of samples and samples treated with multiple conditions, and discuss alternative protocols for carrying out similar analyses with next-generation DNA sequencing data from two different instrument platforms (Illumina GA and Applied Biosystems SOLiD).

This unit describes how to set up and analyze ligand-protein docking calculations using AutoDock and the graphical user interface, AutoDockTools (ADT). The AutoDock scoring function is a subset of the AMBER force field that treats molecules using the United Atom model. The unit uses an X-ray crystal structure of Indinavir bound to HIV-1 protease taken from the Protein Data Bank (UNIT 1.9) and shows how to prepare the ligand and receptor for AutoGrid, which computes grid maps needed by AutoDock. Indinavir is prepared for AutoDock, adding the polar hydrogens, and partial charges, and defining the rotatable bonds that will be explored during the docking. The input files for AutoGrid and AutoDock are created, and then the grid map calculation run, followed by the docking calculation in AutoDock. Finally, this unit describes some of the ways the results can be analyzed using AutoDockTools.

Genome-based peptide fingerprint scanning (GFS) directly maps several types of protein mass spectral (MS) data to the loci in the genome that may have encoded for the protein. This process can be used either for protein identification or for proteogenomic mapping, which is gene-finding and annotation based on proteomic data. Inputs to the program are one or more mass spectrometry files from peptide mass fingerprinting and/or tandem MS (MS/MS) along with one or more sequences to search them against, and the output is the coordinates of any matches found. This unit describes the use of GFS and subsequent results analysis.

The MetaCyc database (http://metacyc.org) is a collection of more than a thousand metabolic pathways from a wide variety of organisms, collected from the literature by manual curation. BioCyc contains more than 370 Pathway/Genome Databases, each of which describes the genome and predicted metabolic pathways of a single organism, as computationally predicted from the annotated genomes, using MetaCyc data as a reference. The protocols in this unit introduce the user to the extensive data content available in MetaCyc and BioCyc, and to mechanisms for querying, visualizing, and analyzing these data using the Pathway Tools software.

Glide is a ligand docking program for predicting protein-ligand binding modes and ranking ligands via high-throughput virtual screening. Glide utilizes two different scoring functions, SP and XP GlideScore, to rank-order compounds. Three modes of sampling ligand conformational and positional degrees of freedom are available to determine the optimal ligand orientation relative to a rigid protein receptor geometry. This unit presents protocols for flexible ligand docking with Glide, optionally including ligand constraints or ligand molecular similarities.

DrugBank is a fully curated drug and drug target database that contains information on nearly 5000 drugs, including > 1200 FDA-approved small molecule and biotech drugs as well as >3200 experimental drugs. Additionally, more than 14,000 protein or drug target sequences are linked to these drug entries. DrugBank is primarily focused on providing both the query/search tools and the biophysical data needed to facilitate drug discovery and drug development. This unit provides readers with a detailed description of how to effectively use the DrugBank database and how to navigate through the DrugBank Web site. It also provides specific examples of how to find chemical homologs of potential drug leads and how to identify potential drug targets from newly sequenced pathogens. The intent of this unit is to give readers some introduction into the field of cheminformatics (the study of chemical information) and to show how cheminformatics can be seamlessly integrated into the field of bioinformatics.

Abstract A genome browser is software that allows users to visualize DNA, protein, or other sequence features within the context of a reference sequence, such as a chromosome or contig. The Generic Genome Browser (GBrowse) is an open‐source browser developed as part of the Generic Model Organism Database project (Stein et al., 2002). GBrowse can be configured to display genomic sequence features for any organism and is the browser used for the model organisms Drosophila melanogaster (Grumbling and Strelets, 2006) and Caenorhabditis elegans (Schwarz et al., 2006), among others. The software package can be downloaded from the web and run on a Windows, Mac OS X, or Unix‐type system. Version 1.64, as described in this protocol, was released in November 2005, but the software is under active development and new versions are released about every six months.